Do you want your online business to succeed? If yes, it is important that you understand your data perfectly. Well, SQL or Structured Query Language can help you with that. This popular programming language is used to pull information from a database.

Though SQL can’t be used to create web applications, it can interact with the relational database systems built into many applications. Knowing how to use SQL lets you retrieve just any kind of data you want to know about your customers and business. When the SQL language has so many benefits why not learn it?

Strata Scratch is a well-known online platform where you can learn SQL and enhance your analytical and marketing skills to a great extent. At Strata Scratch, you can practice SQL and Python with more than 40 problem sets. Isn’t it great?

In terms of marketing and data management, SQL has many advantages over traditional data programs. Suppose you have an Excel Spreadsheet containing every bit of data about your customers such as what plans they are on, when they sign up, how often they purchase your product/service, what marketing campaign they see. In short, every action they take on your website and within your application.

With the help of SQL, you can utilize this data to cohort analysis and see if your targeted audience is using your product more or less over time, to examine different marketing campaigns, to see what actions your users are taking and to figure out the demographics of your targeted audience – that will help you in creating ad campaigns targeting similar demographics.

As business departments are becoming more data-driven nowadays, learning SQL is quite important. Here at Strata Scratch, we teach you the fundamentals of writing SQL queries to pull and process your data.

We have designed our SQL guides and exercises specifically for marketing and business applications. Whether you are a newbie who wants to start the coding right away or an expert eager to learn the latest perspective in data analytics, we can serve you. Additionally, we go through the real-world business and case studies to better understand data-driven analytical methodologies. In short, with us, you can rest assured that you will build your confidence in SQL and data analytics.

For most students, SQL could be among the easiest programming languages to look at because the codes read the same like regular English. You will often find the three magic words SELECT, FROM, and WHERE as the core in database querying. But later on as you progress, you will find more complex statements for joins, aggregations, case statements, and subqueries which can be intimidating for newbies and even intermediate learners. The good news is that SQL shouldn’t scare you at all, especially for business and marketing students who don’t have a coding background. We have tailored our guides and exercises specific for business and marketing applications, hoping to help SQL newbies start coding right away or even experienced users of SQL who wants to learn a new perspective in data analytics.

What You Can Learn from the TutorialsLearning and remembering the lessons are simpler when it is designed to be something intuitive. Our guides and tutorials aim to break the barrier of entry for non-technical or non-developer students and professionals who will find SQL useful in their career. We hope that new and intermediate learners will be able to master the fundamentals of database querying with a lesser amount of time.SQL newbies are expected to learn how to use the keywords SELECT, FROM, and WHERE statements in manipulating data. These are the fundamentals of database management until you progress to the more advanced skills useful in data analytics.To up your skills, you will then have to go through the intermediate level tutorials where you will be introduced to more complex statements and database manipulation techniques. Among the most important topics to learn are common operators for numerical and non-numerical data, how to sort data using the ORDER BY statement, aggregations, how to manipulate rows and columns, how to use GROUP BY with ORDER BY and HAVING phrases, and CASE statements.

Problem Sets and ExercisesThe learning experience wouldn’t be complete without problem sets and case studies you can practice to apply your newly acquired skills. Instead of a generalized approach, we have designed our problem sets that tackle common problems encountered in business and marketing. Here, you will have to use your SQL and analytical skills to manipulate or retrieve data, make visualizations, analyze trends, and make a sound judgement based on your solution.

What’s NewOur problem sets are continuously being updated to constantly challenge your skills! We have included questions that are related to common business problems, such as the Forbes Global exercises, French Employment Exercises, Hotel Reviews Exercises, Library Usage Exercises, QB Stats Exercises, Spotify Exercises, and Yelp Exercises. Case Studies are also available for the more advanced learners. Here, you can use any languages you like aside from SQL to answer open-ended questions. You can use all of these problem sets and case studies for your projects as well. Intermediate and even experienced SQL users can find these problem sets helpful for review and references.We hope that through our tutorials and problem sets, we are able to help you build your confidence in SQL and data analytics. We encourage you to master our guides and exercises to ace in your business and marketing career.

If you are new to data analytics, chances are you might have never heard about Google Colab before. In our Python exercises, we have introduced you to Jupyter notebook which is a popular open-source application where you can create and run live codes, visualizations and perform mathematical operations. However, we have discovered something better which will enhance your learning experience in Python. We have added Google colab as a tool for you to create and share Jupyter notebooks easier without installing anything. This free research tool has a lot of nice features that will interest both students and teachers, especially for sharing works related to machine learning and research.

Why Use Google Colab?Google Colab is a cloud service created for research and machine learning education. It now comes with a GPU which is totally free. We have moved our Python exercises to Google Colab for convenience on both the students and the teacher. Since you can easily share and collaborate your work with others, you can improve your Python skills and easily use the available libraries for your applications. If you are used to working with Jupyter notebooks, you can easily upload and share them without any set-up required.Google Colab can be used with common browsers, such as Chrome and Firefox. Since you don’t have to install any software to work on your codes, you can simply run your notebooks using the browsers of your choice.All your notebooks will be saved in your Google Drive and can be shared like your Google Sheets or Docs. Your shared notebooks will include its full contents (code, text, etc.) except for the virtual machine and custom libraries you’ve used. If you need to share this as well, it is advised that you include the cells that automatically install and load the custom files needed for your application.Another thing that’s great about Google Colab is that it supports both Python 2.7 and Python 3.6. So if you have previously worked on your code using any of these versions, you should be able to upload and run them without any problems. Most of our Python exercises support either of these two versions.​What’s the Difference Between Jupyter and Colaboratory?We have already talked about the main features of Jupyter and Google Colab, but you may have asked what is the difference between the two. First of all, the creation of Google Colab was actually inspired from the open-source Jupyter notebook. Unlike Jupyter, Google Colab allows you to immediately run your codes without any installations required. It works like how you would upload and share your Google Docs or Sheets. Since it allows you to use the cloud service for free, you can take advantage of its GPU and computation capabilities for your projects, especially for machine learning. However, you cannot use it for applications that require long-running computations like cryptocurrency mining. This will automatically result in service unavailability. Google Colab is intended for interactive usage and if you need to continuously run your code for your project, you need to use a local runtime through Colaboratory’s UI.

Data analytical skills are important whether you are aspiring to become a data scientist or simply a student or a professional specializing a different field but somehow needs to deal with a large chunk of data. This page is the ideal place to start learning the basics about database programming and how to manipulate a massive amount of data using our platform. The steps below will first introduce you to SQL Lab and the basic functions you need to know to start using our platform effectively.

The SQL Editor

Log in to your account to see your homepage as shown on the image below. The homepage consists of your account information, settings, tabs, and a number of dropdown menus located on the top toolbar. We will begin by exploring the functionalities of the SQL Lab menu which consists of all the basic tools you need to start your database.

Go ahead and click the SQL Lab menu. Here, you will see various options whether you want to start your SQL queries or upload your tables.

​Under SQL Lab menu, choose SQL Editor.

Your page should look like on the image below. You will notice the configuration bars on the left side, the editor box to type SQL commands, and a number of tabs below the editor.

The SQL editor is a powerful tool that allows you to type SQL commands, build and run queries, create and edit your data, visualize results, and many others.

Accessing Public and Private Datasets​​If you are a new user, you will see an untitled query tab on the left side of the page. Below the tab are configuration options which allow you to choose a database and dataset. A public schema dataset is uploaded by default for your convenience. It is composed of public datasets which you can use as a reference. However, the schema is read-only so you cannot make any changes to it.

You also have access to your own dataset repository under your private schema username where you will have full privileges, such as reading or editing your datasets. Under your private schema, you are given the freedom to move data from other schemas or upload your own CSV file.

Running the SQL Query​Next to the configuration sidebar is a workspace where you can type SQL queries. You can start with basic commands such as SELECT, FROM, WHERE, GROUP BY and ORDER BY. We will learn more about this in the next tutorial. For now, it is enough for you to familiarize the functionality of the SQL editor.

To run a query, simply click on the Run Query box located below the workspace. For example, if you run the query typed on the editor above, you should be able to see the following query result:

Visualizing Your Dataset

You can also have the option to visualize data and turn them into meaningful graphs.

To start, click on the Visualize box located below the Results and Query History tabs.

A pop-up window will appear just like the image below. Here, you are given various options as to how you want to visualize your dataset. Under the Chart Type menu, choose what type of display you want for your plot.

Next to the Chart Type menu is the Datasource Name field. Simple type a name you desire for your plot.

Below the Chart Type menu are check boxes that provide options in visualizing your dataset. Check the desired attributes you wanted to include in your graph. For our example above, under column, we have the option to view the attributes (is_dimension and is_date) for artist and best_artist. The agg_func gives an option as to how you want to manipulate your data.

Click the Visualize button to see the results which will open a new tab.

As shown on the results above, you have further options to rename your chart located above the plot, as well as manipulate and filter your data through the sidebar configurations at the left side of the page.

Saving a CSV File​

If you want to export your dataset to a CSV file, simply click on the .CSV button as shown below.

A pop-up window will appear to download the CSV file. Type the filename you want, choose a folder where you want to save the CSV and click the Save button.

Another option to save a CSV is to click the CSV button found above the chart (after you have built a visualization of your dataset):

Now you have the CSV file saved in your folder, you should be able to open it using a compatible program installed on your computer (such as MS Excel) to view your dataset.

SQL Guides and Exercises​Our platform offers guides and exercises to help you with your journey in learning SQL. The SQL tutorials are designed for absolute beginners with no technical background in programming. The available guides discuss the basic and general concepts of SQL which covers the common syntax needed to get you started right away! We make sure that the steps are very easy to follow and comes with actual images to help you visualize the process. We have also prepared exercises to help you apply what you have learned from the tutorials. These exercises are composed of questions specific for business and marketing students. We hope that through our guides and exercises we are able to ease the barrier of learning SQL for newbies in coding and data analytics.

Nowadays, analytics is being considered as the core of every business. By gathering an immense amount of data from various business channels, we can get an insight into whether our marketing initiatives are indeed successful. The question is how can we turn these raw data into a form that can be understood by marketing professionals?

A few years back, database management has created a gap in the business and marketing world because professionals in this field are often not equipped with coding skills to manage database systems. Today, we are privileged to have access to tools that allow us to make our tasks easier.

StrataScratch is a platform curated to help business and marketing students or professionals make analytics easier even without advanced technical knowledge in programming. The platform is also equipped with features, such as graphical functions, easy-to-use SQL editor, and numerous datasets which you can use as practice or reference for your projects.

We have prepared a number of tutorials and exercises which you can use as a learning tool or a reference for projects. These contents are designed for both beginners and advanced learners. You can visit our website to gain access to all the materials needed to get you started.

Practice Problems for Business and Marketing Analytics

The best way to learn database coding is to actually apply what you have learned from the tutorials. To challenge your skills, we have prepared practice problems and exercises which you can try using our SQL editor. These problems are categorized as basic, intermediate, and advanced so that your learning experience becomes progressive.

​The problems and exercises challenge your basic skills in SQL and Python which includes applying basic syntax to access, transfer, and manipulate data. Some questions will also require you to visualize the result for you to gain insights and be able to find an answer to the problem.

Python and SQL Tutorials

We have added more tutorials to enhance your learning experience in Python and SQL. For example, we have included topics that cover SQL techniques for business analytics, data cleaning using Pandas, lambda functions and list comprehensions in Python, merging dataframes for data analysis, NumPy, and many others.

Access to Exercises Written in Python and SQL​

A lot of the exercises on our page are written in Python and SQL. For convenience, the exercises can be accessed through Google CoLab links and run using Jupyter notebooks. You don’t need to install any software since the code will run online. You may also have the option to download the files in ipynb format so you can open it offline using any compatible software installed in your computer.

Solve Case Studies with Any Programming Tools​

We have included case studies about business, medical appointments, and machine learning for those who are up for more challenge. These case studies are designed with open-ended questions to test your analysis and judgment. Moreover, you are given the choice to use any of your favorite tools, such as Python, R, and Tableau.

How to Access our Guides, Problem Sets, and Case Studies​

All our guides, practice problems, and case studies can be accessed under the Education tab of our website. Choose a category which you want to access to. This will lead you to our page with a list of available tutorials and exercises. Clicking a guide or exercise will open a new page. The guide contains the step by step tutorials about your chosen topic, such as how to use Strata Scratch and the basic tutorial on SQL. When you click on the exercises, a new page will open containing the questions linked to our Github and the solutions linked to our Google CoLab.

We hope you will find all of these materials and practice sets useful to enhance your data analytics skills. Enjoy!

If you’re a professional in the workforce, you’ve probably seen a cultural shift towards making data-driven decisions. If you’re a student, you’ve probably seen new classes teaching business and marketing majors new data analytical tools and coding languages. You don’t need us to convince you that new skills and capabilities are needed to stay competitive in the workforce, especially in business and marketing functions.But which skills or capability do I need to learn to stay competitive? It’s been said that engineers have the technical skills necessary to analyze the data but don’t know how to ask the right questions. Business professionals ask the right questions but don’t know how to analyze the vast amount of data. The ideal case is to obviously find a person that can ask the right business questions and analyze the data.So, again, which skills or capabilities do I need to learn? Almost all data these days are stored in a database. So you can learn any tool that can take data out of the database and analyze it. But most common tool/language is SQL but there are also other tools out there like python, R, and Tableau. Once the data is out of the database, you’ll need to put on your business hat to analyze the dataset in a meaningful way.We have some guides to help you along your journey. You should get started right away!

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Here are the some reasons why understanding databases and having data analytical skills are useful in business analytics:

Easily Access and Manipulate DataAs more and more data come on your way, there’s no better option than having a database system. Here, you can just pull out the specific information you need from a vast ocean of data. Simply type a query to get what you need – sounds more convenient, right? An SQL database keeps your data organized and speed up data processing. Having a database management system definitely makes your life easier!

Analyze Data with Only A Few CommandsThere’s no easier way to analyze trends than visualizing them with colorful details! By adding the parameters you need and setting up the graphics command, your data instantly comes to life. In business, understanding trends is very important. You will be updated with new insights and come up with wiser decisions for your business operations.

Help You Support Prototyping, Data Management, and ReportingWith the advancement of technology, a lot of systems being used in businesses today are already automated. A few basic skills in SQL will help you understand the data and provide you the information you want to extract from the system. This in return makes it easier for you to create analysis and reports, as well as build prototypes for your projects or case studies.

More Opportunities in the Business WorldAs a business and marketing student or professional, having analytical skills allows you to gather insights from large sets of data. As large organizations continue to flourish, more and more companies use automated database systems to efficiently run business operations. Being able to navigate the vast amount of data will definitely help you answer complex business questions.

So, what’s next?Learning SQL and other powerful tools/languages does not really require you to be a hardcore software engineer. All you have to know are the basic functionalities on how to select particular rows or columns of data, the basic mathematical operations to manipulate data, and some simple queries to visualize the information you have.

If you want to learn the basics of SQL, just go to our tutorial. You will find an easy-to-follow guide with all the basics you need to start learning SQL right away. Moreover, Strata Scratch provides a user-friendly platform you can use to practice writing simple queries. The platform also provides sample datasets which you can use in designing your prototype or answering your case studies.

Happy analyzing! And if you have any questions, email us at team@stratascratch.com.

In the business world, data is everything. We use data to help us understand the market and our customer. Successful businesses are data-driven and integrate their data-driven decision making into their business processes. We’ve seen the continuous growth of the tech industry – think Facebook and Google – in part due to their data-driven approach to building and growing products.

Data itself has been democratized. Companies collect vast amounts of internal and external data. Data engineering and science teams are formed to help turn data into insights, and it’s becoming apparent that if you want to stay competitive in your career, you will need to develop enough technical capabilities to analyze large amounts of data using sophisticated and powerful tools beyond Excel.

How does a non-technical business and marketing professional build powerful analytical skills? Most books and tools are written by engineers for engineers. We, at Strata Scratch, understand this problem. It’s impossible to find resources tailored to the non-technical professionals – there are no tools easy enough for novices; there are no guides and exercises written for business and marketing professionals that also leverage these powerful tools.​We hope to help the new business and marketing professionals. We have a tool easy enough for non-technical novices to use, with SQL and Python guides and exercises tailored to the business and marketing professional. We’ll keep posting here with useful advice for the data-driven business and marketing professionals. We hope you follow along with us.